The algorithm that aims to minimize the loss function for the supervised learning algorithms, including neural networks. It calculates the gradient, e.g. using backpropagation, and then changes the parameters of the models in the negative gradient direction. It works iteratively and typically uses a mini-batch of training samples at a moment.
Published in Chapter:
Teeth and Landmarks Detection and Classification Based on Deep Neural Networks
Lyudmila N. Tuzova (Denti.AI, Russia), Dmitry V. Tuzoff (Steklov Institute of Mathematics in St. Petersburg, Russia), Sergey I. Nikolenko (Steklov Institute of Mathematics in St. Petersburg, Russia), and Alexey S. Krasnov (Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology, and Immunology, Russia)
Copyright: © 2019
|Pages: 22
DOI: 10.4018/978-1-5225-6243-6.ch006
Abstract
In the recent decade, deep neural networks have enjoyed rapid development in various domains, including medicine. Convolutional neural networks (CNNs), deep neural network structures commonly used for image interpretation, brought the breakthrough in computer vision and became state-of-the-art techniques for various image recognition tasks, such as image classification, object detection, and semantic segmentation. In this chapter, the authors provide an overview of deep learning algorithms and review available literature for dental image analysis with methods based on CNNs. The present study is focused on the problems of landmarks and teeth detection and classification, as these tasks comprise an essential part of dental image interpretation both in clinical dentistry and in human identification systems based on the dental biometrical information.